In machine translation, document alignment refers to finding correspondences between documents which are exact translations of each other. We define pseudo-alignment as the task of finding topical-as opposed to exact-correspondences between documents in different languages. We apply semisupervised methods to pseudo-align multilingual corpora. Specifically, we construct a topic-based graph for each language. Then, given exact correspondences between a subset of documents, we project the unaligned documents into a shared lower-dimensional space. We demonstrate that close documents in this lower-dimensional space tend to share the same topic. This has applications in machine translation and cross-lingual information analysis. Experimental results show that pseudo-alignment of multilingual corpora is feasible and that the document alignments produced are qualitatively sound. Our technique requires no linguistic knowledge of the corpus. On average when 10% of the corpus consists of exact correspondences, an on-topic correspondence occurs within the top 5 foreign neighbors in the lower-dimensional space while the exact correspondence occurs within the top 10 foreign neighbors in this this space. We also show how to substantially improve these results with a novel method for incorporating language-independent information.